In　this　article,　we　focus　on　developing　a　neural-network-based　critic　learning　strategy　toward　robust　dynamic　stabilization　for　a　class　of　uncertain　nonlinear　systems.　A　type　of　general　uncertainties　involved　both　in　the　internal　dynamics　and　in　the　input　matrix　is　considered.　An　auxiliary　system　with　actual　action　and　auxiliary　signal　is　constructed　after　dynamics　decomposition　and　combination　for　the　original　plant.　The　reasonability　of　the　control　problem　transformation　from　robust　stabilization　to　optimal　feedback　design　is　also　provided　theoretically.　After　that,　the　adaptive　critic　learning　method　based　on　a　neural　network　is　established　to　derive　the　approximate　optimal　solution　of　the　transformed　control　problem.　The　critic　weight　can　be　initialized　to　a　zero　vector,　which　apparently　facilitates　the　learning　process.　Numerical　simulation　is　finally　presented　to　illustrate　the　effectiveness　of　the　critic　learning　approach　for　neural　robust　stabilization.